Overview

Dataset statistics

Number of variables81
Number of observations18169
Missing cells2340
Missing cells (%)0.2%
Duplicate rows290
Duplicate rows (%)1.6%
Total size in memory12.3 MiB
Average record size in memory707.3 B

Variable types

Numeric13
Categorical68

Alerts

Dataset has 290 (1.6%) duplicate rowsDuplicates
outcome is highly imbalanced (60.6%)Imbalance
protocol_type_udp is highly imbalanced (52.0%)Imbalance
service_X11 is highly imbalanced (99.7%)Imbalance
service_Z39_50 is highly imbalanced (94.1%)Imbalance
service_auth is highly imbalanced (93.5%)Imbalance
service_bgp is highly imbalanced (95.8%)Imbalance
service_courier is highly imbalanced (95.0%)Imbalance
service_csnet_ns is highly imbalanced (96.4%)Imbalance
service_ctf is highly imbalanced (96.0%)Imbalance
service_daytime is highly imbalanced (96.6%)Imbalance
service_discard is highly imbalanced (96.4%)Imbalance
service_domain is highly imbalanced (96.8%)Imbalance
service_domain_u is highly imbalanced (66.7%)Imbalance
service_echo is highly imbalanced (97.9%)Imbalance
service_eco_i is highly imbalanced (92.3%)Imbalance
service_ecr_i is highly imbalanced (86.9%)Imbalance
service_efs is highly imbalanced (97.2%)Imbalance
service_exec is highly imbalanced (97.0%)Imbalance
service_finger is highly imbalanced (89.4%)Imbalance
service_ftp is highly imbalanced (89.8%)Imbalance
service_ftp_data is highly imbalanced (70.6%)Imbalance
service_gopher is highly imbalanced (95.5%)Imbalance
service_hostnames is highly imbalanced (96.7%)Imbalance
service_http_443 is highly imbalanced (96.4%)Imbalance
service_http_8001 is highly imbalanced (99.9%)Imbalance
service_imap4 is highly imbalanced (95.8%)Imbalance
service_iso_tsap is highly imbalanced (95.7%)Imbalance
service_klogin is highly imbalanced (97.0%)Imbalance
service_kshell is highly imbalanced (97.9%)Imbalance
service_ldap is highly imbalanced (97.0%)Imbalance
service_link is highly imbalanced (96.6%)Imbalance
service_login is highly imbalanced (96.3%)Imbalance
service_mtp is highly imbalanced (96.8%)Imbalance
service_name is highly imbalanced (97.0%)Imbalance
service_netbios_dgm is highly imbalanced (96.9%)Imbalance
service_netbios_ns is highly imbalanced (97.8%)Imbalance
service_netbios_ssn is highly imbalanced (97.2%)Imbalance
service_netstat is highly imbalanced (97.6%)Imbalance
service_nnsp is highly imbalanced (96.0%)Imbalance
service_nntp is highly imbalanced (97.7%)Imbalance
service_ntp_u is highly imbalanced (98.7%)Imbalance
service_other is highly imbalanced (82.6%)Imbalance
service_pm_dump is highly imbalanced (99.8%)Imbalance
service_pop_2 is highly imbalanced (99.4%)Imbalance
service_pop_3 is highly imbalanced (98.0%)Imbalance
service_printer is highly imbalanced (99.3%)Imbalance
service_red_i is highly imbalanced (99.8%)Imbalance
service_remote_job is highly imbalanced (99.1%)Imbalance
service_rje is highly imbalanced (99.6%)Imbalance
service_shell is highly imbalanced (99.5%)Imbalance
service_smtp is highly imbalanced (67.9%)Imbalance
service_sql_net is highly imbalanced (98.4%)Imbalance
service_ssh is highly imbalanced (97.8%)Imbalance
service_sunrpc is highly imbalanced (96.8%)Imbalance
service_supdup is highly imbalanced (96.0%)Imbalance
service_systat is highly imbalanced (96.7%)Imbalance
service_telnet is highly imbalanced (86.1%)Imbalance
service_tim_i is highly imbalanced (99.8%)Imbalance
service_time is highly imbalanced (95.8%)Imbalance
service_urp_i is highly imbalanced (95.8%)Imbalance
service_uucp is highly imbalanced (95.2%)Imbalance
service_uucp_path is highly imbalanced (95.7%)Imbalance
service_vmnet is highly imbalanced (95.7%)Imbalance
service_whois is highly imbalanced (96.3%)Imbalance
outcome has 2340 (12.9%) missing valuesMissing
src_bytes is highly skewed (γ1 = 27.27853944)Skewed
dst_bytes is highly skewed (γ1 = 134.787018)Skewed
src_bytes has 6035 (33.2%) zerosZeros
dst_bytes has 10292 (56.6%) zerosZeros
serror_rate has 13405 (73.8%) zerosZeros
same_srv_rate has 287 (1.6%) zerosZeros
diff_srv_rate has 12028 (66.2%) zerosZeros
srv_diff_host_rate has 14680 (80.8%) zerosZeros
dst_host_same_srv_rate has 840 (4.6%) zerosZeros
dst_host_diff_srv_rate has 5827 (32.1%) zerosZeros
dst_host_srv_diff_host_rate has 13274 (73.1%) zerosZeros
dst_host_serror_rate has 12616 (69.4%) zerosZeros
dst_host_srv_serror_rate has 13128 (72.3%) zerosZeros

Reproduction

Analysis started2024-10-01 13:15:24.390144
Analysis finished2024-10-01 13:15:44.260739
Duration19.87 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

src_bytes
Real number (ℝ)

SKEWED  ZEROS 

Distinct1408
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7547.9261
Minimum0
Maximum5135678
Zeros6035
Zeros (%)33.2%
Negative0
Negative (%)0.0%
Memory size283.9 KiB
2024-10-01T15:15:44.365490image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median48
Q3256
95-th percentile1480
Maximum5135678
Range5135678
Interquartile range (IQR)256

Descriptive statistics

Standard deviation147484.44
Coefficient of variation (CV)19.53973
Kurtosis836.9145
Mean7547.9261
Median Absolute Deviation (MAD)48
Skewness27.278539
Sum1.3713827 × 108
Variance2.175166 × 1010
MonotonicityNot monotonic
2024-10-01T15:15:44.545526image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 6035
33.2%
48 2367
 
13.0%
44 302
 
1.7%
1 255
 
1.4%
45 250
 
1.4%
1032 212
 
1.2%
46 170
 
0.9%
43 143
 
0.8%
105 136
 
0.7%
54540 124
 
0.7%
Other values (1398) 8175
45.0%
ValueCountFrequency (%)
0 6035
33.2%
1 255
 
1.4%
4 1
 
< 0.1%
5 3
 
< 0.1%
6 18
 
0.1%
7 15
 
0.1%
8 117
 
0.6%
9 29
 
0.2%
10 31
 
0.2%
11 12
 
0.1%
ValueCountFrequency (%)
5135678 5
 
< 0.1%
5133876 4
 
< 0.1%
4357454 1
 
< 0.1%
2194620 1
 
< 0.1%
2194619 26
0.1%
2131840 1
 
< 0.1%
501760 17
0.1%
417488 1
 
< 0.1%
250728 1
 
< 0.1%
250726 1
 
< 0.1%

dst_bytes
Real number (ℝ)

SKEWED  ZEROS 

Distinct3110
Distinct (%)17.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean74625.152
Minimum0
Maximum1.3099374 × 109
Zeros10292
Zeros (%)56.6%
Negative0
Negative (%)0.0%
Memory size283.9 KiB
2024-10-01T15:15:44.675810image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3450
95-th percentile8314
Maximum1.3099374 × 109
Range1.3099374 × 109
Interquartile range (IQR)450

Descriptive statistics

Standard deviation9718293.6
Coefficient of variation (CV)130.22813
Kurtosis18168.025
Mean74625.152
Median Absolute Deviation (MAD)0
Skewness134.78702
Sum1.3558644 × 109
Variance9.4445231 × 1013
MonotonicityNot monotonic
2024-10-01T15:15:44.811164image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 10292
56.6%
105 164
 
0.9%
8314 124
 
0.7%
332 81
 
0.4%
330 77
 
0.4%
331 73
 
0.4%
327 64
 
0.4%
328 62
 
0.3%
334 60
 
0.3%
44 56
 
0.3%
Other values (3100) 7116
39.2%
ValueCountFrequency (%)
0 10292
56.6%
1 2
 
< 0.1%
4 28
 
0.2%
14 1
 
< 0.1%
15 6
 
< 0.1%
17 4
 
< 0.1%
18 1
 
< 0.1%
26 5
 
< 0.1%
27 1
 
< 0.1%
29 5
 
< 0.1%
ValueCountFrequency (%)
1309937401 1
< 0.1%
5150841 1
< 0.1%
2414928 1
< 0.1%
1632218 1
< 0.1%
1587397 1
< 0.1%
1465775 1
< 0.1%
1324486 1
< 0.1%
1285078 1
< 0.1%
954639 1
< 0.1%
759161 1
< 0.1%

logged_in
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
11238 
1.0
6931 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters54507
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row1.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 11238
61.9%
1.0 6931
38.1%

Length

2024-10-01T15:15:44.958979image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-01T15:15:45.042306image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 11238
61.9%
1.0 6931
38.1%

Most occurring characters

ValueCountFrequency (%)
0 29407
54.0%
. 18169
33.3%
1 6931
 
12.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 29407
54.0%
. 18169
33.3%
1 6931
 
12.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 29407
54.0%
. 18169
33.3%
1 6931
 
12.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 29407
54.0%
. 18169
33.3%
1 6931
 
12.7%

serror_rate
Real number (ℝ)

ZEROS 

Distinct65
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.23943915
Minimum0
Maximum1
Zeros13405
Zeros (%)73.8%
Negative0
Negative (%)0.0%
Memory size283.9 KiB
2024-10-01T15:15:45.140136image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.1
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.1

Descriptive statistics

Standard deviation0.42207947
Coefficient of variation (CV)1.7627838
Kurtosis-0.4678534
Mean0.23943915
Median Absolute Deviation (MAD)0
Skewness1.2291375
Sum4350.37
Variance0.17815108
MonotonicityNot monotonic
2024-10-01T15:15:45.908536image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 13405
73.8%
1 4158
 
22.9%
0.5 67
 
0.4%
0.33 49
 
0.3%
0.99 37
 
0.2%
0.07 35
 
0.2%
0.01 34
 
0.2%
0.06 28
 
0.2%
0.25 27
 
0.1%
0.05 26
 
0.1%
Other values (55) 303
 
1.7%
ValueCountFrequency (%)
0 13405
73.8%
0.01 34
 
0.2%
0.02 8
 
< 0.1%
0.03 14
 
0.1%
0.04 17
 
0.1%
0.05 26
 
0.1%
0.06 28
 
0.2%
0.07 35
 
0.2%
0.08 25
 
0.1%
0.09 21
 
0.1%
ValueCountFrequency (%)
1 4158
22.9%
0.99 37
 
0.2%
0.98 6
 
< 0.1%
0.97 8
 
< 0.1%
0.96 5
 
< 0.1%
0.95 3
 
< 0.1%
0.94 5
 
< 0.1%
0.93 3
 
< 0.1%
0.92 3
 
< 0.1%
0.91 1
 
< 0.1%

same_srv_rate
Real number (ℝ)

ZEROS 

Distinct95
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.71376025
Minimum0
Maximum1
Zeros287
Zeros (%)1.6%
Negative0
Negative (%)0.0%
Memory size283.9 KiB
2024-10-01T15:15:46.008693image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.02
Q10.15
median1
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.85

Descriptive statistics

Standard deviation0.41992995
Coefficient of variation (CV)0.58833474
Kurtosis-1.2316228
Mean0.71376025
Median Absolute Deviation (MAD)0
Skewness-0.83611334
Sum12968.31
Variance0.17634117
MonotonicityNot monotonic
2024-10-01T15:15:46.111175image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 12104
66.6%
0.01 480
 
2.6%
0.03 413
 
2.3%
0.07 399
 
2.2%
0.02 393
 
2.2%
0.06 391
 
2.2%
0.04 377
 
2.1%
0.05 358
 
2.0%
0.08 349
 
1.9%
0 287
 
1.6%
Other values (85) 2618
 
14.4%
ValueCountFrequency (%)
0 287
1.6%
0.01 480
2.6%
0.02 393
2.2%
0.03 413
2.3%
0.04 377
2.1%
0.05 358
2.0%
0.06 391
2.2%
0.07 399
2.2%
0.08 349
1.9%
0.09 263
1.4%
ValueCountFrequency (%)
1 12104
66.6%
0.99 103
 
0.6%
0.98 12
 
0.1%
0.97 10
 
0.1%
0.96 3
 
< 0.1%
0.95 1
 
< 0.1%
0.93 4
 
< 0.1%
0.92 6
 
< 0.1%
0.91 7
 
< 0.1%
0.9 3
 
< 0.1%

diff_srv_rate
Real number (ℝ)

ZEROS 

Distinct76
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.055157136
Minimum0
Maximum1
Zeros12028
Zeros (%)66.2%
Negative0
Negative (%)0.0%
Memory size283.9 KiB
2024-10-01T15:15:46.209371image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.06
95-th percentile0.22
Maximum1
Range1
Interquartile range (IQR)0.06

Descriptive statistics

Standard deviation0.1690935
Coefficient of variation (CV)3.0656686
Kurtosis21.877277
Mean0.055157136
Median Absolute Deviation (MAD)0
Skewness4.6668253
Sum1002.15
Variance0.028592612
MonotonicityNot monotonic
2024-10-01T15:15:46.310080image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 12028
66.2%
0.06 2122
 
11.7%
0.07 1195
 
6.6%
0.05 893
 
4.9%
1 415
 
2.3%
0.08 243
 
1.3%
0.01 125
 
0.7%
0.09 101
 
0.6%
0.04 88
 
0.5%
0.5 74
 
0.4%
Other values (66) 885
 
4.9%
ValueCountFrequency (%)
0 12028
66.2%
0.01 125
 
0.7%
0.02 37
 
0.2%
0.03 38
 
0.2%
0.04 88
 
0.5%
0.05 893
 
4.9%
0.06 2122
 
11.7%
0.07 1195
 
6.6%
0.08 243
 
1.3%
0.09 101
 
0.6%
ValueCountFrequency (%)
1 415
2.3%
0.99 4
 
< 0.1%
0.97 2
 
< 0.1%
0.96 5
 
< 0.1%
0.95 6
 
< 0.1%
0.89 1
 
< 0.1%
0.82 2
 
< 0.1%
0.8 2
 
< 0.1%
0.79 1
 
< 0.1%
0.76 1
 
< 0.1%

srv_diff_host_rate
Real number (ℝ)

ZEROS 

Distinct53
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.071883428
Minimum0
Maximum1
Zeros14680
Zeros (%)80.8%
Negative0
Negative (%)0.0%
Memory size283.9 KiB
2024-10-01T15:15:46.408787image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.67
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.21851168
Coefficient of variation (CV)3.0398061
Kurtosis11.357582
Mean0.071883428
Median Absolute Deviation (MAD)0
Skewness3.4982044
Sum1306.05
Variance0.047747355
MonotonicityNot monotonic
2024-10-01T15:15:46.529273image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 14680
80.8%
1 751
 
4.1%
0.01 361
 
2.0%
0.5 135
 
0.7%
0.67 125
 
0.7%
0.33 116
 
0.6%
0.12 114
 
0.6%
0.02 111
 
0.6%
0.1 103
 
0.6%
0.11 97
 
0.5%
Other values (43) 1576
 
8.7%
ValueCountFrequency (%)
0 14680
80.8%
0.01 361
 
2.0%
0.02 111
 
0.6%
0.03 37
 
0.2%
0.04 38
 
0.2%
0.05 33
 
0.2%
0.06 84
 
0.5%
0.07 77
 
0.4%
0.08 92
 
0.5%
0.09 90
 
0.5%
ValueCountFrequency (%)
1 751
4.1%
0.83 3
 
< 0.1%
0.8 12
 
0.1%
0.75 29
 
0.2%
0.71 3
 
< 0.1%
0.67 125
 
0.7%
0.62 1
 
< 0.1%
0.6 23
 
0.1%
0.57 9
 
< 0.1%
0.56 1
 
< 0.1%

dst_host_count
Real number (ℝ)

Distinct255
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean191.99119
Minimum1
Maximum255
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size283.9 KiB
2024-10-01T15:15:46.726908image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q1115
median255
Q3255
95-th percentile255
Maximum255
Range254
Interquartile range (IQR)140

Descriptive statistics

Standard deviation93.84319
Coefficient of variation (CV)0.48878904
Kurtosis-0.65227081
Mean191.99119
Median Absolute Deviation (MAD)0
Skewness-1.0379386
Sum3488288
Variance8806.5444
MonotonicityNot monotonic
2024-10-01T15:15:46.890552image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
255 11458
63.1%
1 240
 
1.3%
2 189
 
1.0%
3 160
 
0.9%
4 124
 
0.7%
5 100
 
0.6%
6 100
 
0.6%
8 92
 
0.5%
9 89
 
0.5%
7 85
 
0.5%
Other values (245) 5532
30.4%
ValueCountFrequency (%)
1 240
1.3%
2 189
1.0%
3 160
0.9%
4 124
0.7%
5 100
0.6%
6 100
0.6%
7 85
 
0.5%
8 92
 
0.5%
9 89
 
0.5%
10 72
 
0.4%
ValueCountFrequency (%)
255 11458
63.1%
254 11
 
0.1%
253 12
 
0.1%
252 9
 
< 0.1%
251 9
 
< 0.1%
250 11
 
0.1%
249 6
 
< 0.1%
248 9
 
< 0.1%
247 13
 
0.1%
246 11
 
0.1%

dst_host_same_srv_rate
Real number (ℝ)

ZEROS 

Distinct101
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.52854973
Minimum0
Maximum1
Zeros840
Zeros (%)4.6%
Negative0
Negative (%)0.0%
Memory size283.9 KiB
2024-10-01T15:15:47.010651image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.01
Q10.06
median0.54
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.94

Descriptive statistics

Standard deviation0.41511787
Coefficient of variation (CV)0.78539038
Kurtosis-1.7022646
Mean0.52854973
Median Absolute Deviation (MAD)0.46
Skewness-0.04412094
Sum9603.22
Variance0.17232285
MonotonicityNot monotonic
2024-10-01T15:15:47.125738image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 6060
33.4%
0.54 2363
 
13.0%
0 840
 
4.6%
0.01 838
 
4.6%
0.02 783
 
4.3%
0.07 667
 
3.7%
0.04 656
 
3.6%
0.05 591
 
3.3%
0.03 486
 
2.7%
0.06 442
 
2.4%
Other values (91) 4443
24.5%
ValueCountFrequency (%)
0 840
4.6%
0.01 838
4.6%
0.02 783
4.3%
0.03 486
2.7%
0.04 656
3.6%
0.05 591
3.3%
0.06 442
2.4%
0.07 667
3.7%
0.08 360
2.0%
0.09 213
 
1.2%
ValueCountFrequency (%)
1 6060
33.4%
0.99 124
 
0.7%
0.98 97
 
0.5%
0.97 67
 
0.4%
0.96 98
 
0.5%
0.95 90
 
0.5%
0.94 51
 
0.3%
0.93 50
 
0.3%
0.92 51
 
0.3%
0.91 47
 
0.3%

dst_host_diff_srv_rate
Real number (ℝ)

ZEROS 

Distinct101
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.070203093
Minimum0
Maximum1
Zeros5827
Zeros (%)32.1%
Negative0
Negative (%)0.0%
Memory size283.9 KiB
2024-10-01T15:15:47.244995image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.02
Q30.07
95-th percentile0.4
Maximum1
Range1
Interquartile range (IQR)0.07

Descriptive statistics

Standard deviation0.16725899
Coefficient of variation (CV)2.3825017
Kurtosis17.456433
Mean0.070203093
Median Absolute Deviation (MAD)0.02
Skewness4.15262
Sum1275.52
Variance0.02797557
MonotonicityNot monotonic
2024-10-01T15:15:47.371917image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5827
32.1%
0.02 3269
18.0%
0.07 1989
 
10.9%
0.01 1229
 
6.8%
0.06 1167
 
6.4%
0.05 944
 
5.2%
0.08 861
 
4.7%
0.03 535
 
2.9%
0.04 420
 
2.3%
0.09 354
 
1.9%
Other values (91) 1574
 
8.7%
ValueCountFrequency (%)
0 5827
32.1%
0.01 1229
 
6.8%
0.02 3269
18.0%
0.03 535
 
2.9%
0.04 420
 
2.3%
0.05 944
 
5.2%
0.06 1167
 
6.4%
0.07 1989
 
10.9%
0.08 861
 
4.7%
0.09 354
 
1.9%
ValueCountFrequency (%)
1 229
1.3%
0.99 5
 
< 0.1%
0.98 2
 
< 0.1%
0.97 9
 
< 0.1%
0.96 8
 
< 0.1%
0.95 12
 
0.1%
0.94 6
 
< 0.1%
0.93 9
 
< 0.1%
0.92 4
 
< 0.1%
0.91 10
 
0.1%

dst_host_srv_diff_host_rate
Real number (ℝ)

ZEROS 

Distinct57
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.018876658
Minimum0
Maximum1
Zeros13274
Zeros (%)73.1%
Negative0
Negative (%)0.0%
Memory size283.9 KiB
2024-10-01T15:15:47.493707image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.01
95-th percentile0.07
Maximum1
Range1
Interquartile range (IQR)0.01

Descriptive statistics

Standard deviation0.077775933
Coefficient of variation (CV)4.1202173
Kurtosis93.592293
Mean0.018876658
Median Absolute Deviation (MAD)0
Skewness8.7900132
Sum342.97
Variance0.0060490958
MonotonicityNot monotonic
2024-10-01T15:15:47.621250image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 13274
73.1%
0.02 1095
 
6.0%
0.01 982
 
5.4%
0.03 647
 
3.6%
0.04 581
 
3.2%
0.05 447
 
2.5%
0.06 183
 
1.0%
0.07 143
 
0.8%
0.08 73
 
0.4%
1 59
 
0.3%
Other values (47) 685
 
3.8%
ValueCountFrequency (%)
0 13274
73.1%
0.01 982
 
5.4%
0.02 1095
 
6.0%
0.03 647
 
3.6%
0.04 581
 
3.2%
0.05 447
 
2.5%
0.06 183
 
1.0%
0.07 143
 
0.8%
0.08 73
 
0.4%
0.09 58
 
0.3%
ValueCountFrequency (%)
1 59
0.3%
0.75 2
 
< 0.1%
0.67 16
 
0.1%
0.6 6
 
< 0.1%
0.57 1
 
< 0.1%
0.56 3
 
< 0.1%
0.55 1
 
< 0.1%
0.54 6
 
< 0.1%
0.53 6
 
< 0.1%
0.52 9
 
< 0.1%

dst_host_serror_rate
Real number (ℝ)

ZEROS 

Distinct99
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.24010898
Minimum0
Maximum1
Zeros12616
Zeros (%)69.4%
Negative0
Negative (%)0.0%
Memory size283.9 KiB
2024-10-01T15:15:47.744822image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.12
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.12

Descriptive statistics

Standard deviation0.42080964
Coefficient of variation (CV)1.7525777
Kurtosis-0.46749903
Mean0.24010898
Median Absolute Deviation (MAD)0
Skewness1.2282564
Sum4362.54
Variance0.17708076
MonotonicityNot monotonic
2024-10-01T15:15:47.869167image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 12616
69.4%
1 4047
 
22.3%
0.01 478
 
2.6%
0.02 149
 
0.8%
0.03 104
 
0.6%
0.99 46
 
0.3%
0.09 45
 
0.2%
0.05 43
 
0.2%
0.04 42
 
0.2%
0.08 39
 
0.2%
Other values (89) 560
 
3.1%
ValueCountFrequency (%)
0 12616
69.4%
0.01 478
 
2.6%
0.02 149
 
0.8%
0.03 104
 
0.6%
0.04 42
 
0.2%
0.05 43
 
0.2%
0.06 26
 
0.1%
0.07 26
 
0.1%
0.08 39
 
0.2%
0.09 45
 
0.2%
ValueCountFrequency (%)
1 4047
22.3%
0.99 46
 
0.3%
0.98 24
 
0.1%
0.97 15
 
0.1%
0.96 13
 
0.1%
0.95 12
 
0.1%
0.94 11
 
0.1%
0.93 10
 
0.1%
0.92 5
 
< 0.1%
0.91 9
 
< 0.1%

dst_host_srv_serror_rate
Real number (ℝ)

ZEROS 

Distinct75
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.23418735
Minimum0
Maximum1
Zeros13128
Zeros (%)72.3%
Negative0
Negative (%)0.0%
Memory size283.9 KiB
2024-10-01T15:15:47.991608image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.01
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.01

Descriptive statistics

Standard deviation0.42070269
Coefficient of variation (CV)1.7964364
Kurtosis-0.40521768
Mean0.23418735
Median Absolute Deviation (MAD)0
Skewness1.2585214
Sum4254.95
Variance0.17699075
MonotonicityNot monotonic
2024-10-01T15:15:48.123382image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 13128
72.3%
1 4123
 
22.7%
0.01 538
 
3.0%
0.02 87
 
0.5%
0.03 25
 
0.1%
0.5 16
 
0.1%
0.04 16
 
0.1%
0.07 13
 
0.1%
0.97 12
 
0.1%
0.11 11
 
0.1%
Other values (65) 200
 
1.1%
ValueCountFrequency (%)
0 13128
72.3%
0.01 538
 
3.0%
0.02 87
 
0.5%
0.03 25
 
0.1%
0.04 16
 
0.1%
0.05 10
 
0.1%
0.06 9
 
< 0.1%
0.07 13
 
0.1%
0.08 4
 
< 0.1%
0.09 10
 
0.1%
ValueCountFrequency (%)
1 4123
22.7%
0.98 9
 
< 0.1%
0.97 12
 
0.1%
0.96 5
 
< 0.1%
0.95 5
 
< 0.1%
0.94 3
 
< 0.1%
0.93 4
 
< 0.1%
0.92 2
 
< 0.1%
0.91 1
 
< 0.1%
0.9 3
 
< 0.1%

level
Real number (ℝ)

Distinct22
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.774286
Minimum0
Maximum21
Zeros11
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size283.9 KiB
2024-10-01T15:15:48.231367image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile16
Q119
median21
Q321
95-th percentile21
Maximum21
Range21
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.1989017
Coefficient of variation (CV)0.11120006
Kurtosis18.019232
Mean19.774286
Median Absolute Deviation (MAD)0
Skewness-3.4718714
Sum359279
Variance4.8351688
MonotonicityNot monotonic
2024-10-01T15:15:48.324990image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
21 10483
57.7%
18 2590
 
14.3%
20 2466
 
13.6%
19 1328
 
7.3%
17 300
 
1.7%
15 287
 
1.6%
16 195
 
1.1%
12 100
 
0.6%
11 100
 
0.6%
14 78
 
0.4%
Other values (12) 242
 
1.3%
ValueCountFrequency (%)
0 11
0.1%
1 9
 
< 0.1%
2 8
 
< 0.1%
3 10
 
0.1%
4 12
0.1%
5 8
 
< 0.1%
6 17
0.1%
7 17
0.1%
8 10
 
0.1%
9 25
0.1%
ValueCountFrequency (%)
21 10483
57.7%
20 2466
 
13.6%
19 1328
 
7.3%
18 2590
 
14.3%
17 300
 
1.7%
16 195
 
1.1%
15 287
 
1.6%
14 78
 
0.4%
13 76
 
0.4%
12 100
 
0.6%

outcome
Categorical

IMBALANCE  MISSING 

Distinct18
Distinct (%)0.1%
Missing2340
Missing (%)12.9%
Memory size1.2 MiB
normal
9109 
neptune
5050 
satan
 
452
portsweep
 
286
smurf
 
265
Other values (13)
 
667

Length

Max length15
Median length6
Mean length6.3604776
Min length3

Characters and Unicode

Total characters100680
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowneptune
2nd rowneptune
3rd rownormal
4th rownormal
5th rownormal

Common Values

ValueCountFrequency (%)
normal 9109
50.1%
neptune 5050
27.8%
satan 452
 
2.5%
portsweep 286
 
1.6%
smurf 265
 
1.5%
ipsweep 130
 
0.7%
back 130
 
0.7%
warezclient 123
 
0.7%
teardrop 118
 
0.6%
nmap 108
 
0.6%
Other values (8) 58
 
0.3%
(Missing) 2340
 
12.9%

Length

2024-10-01T15:15:48.426122image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
normal 9109
57.5%
neptune 5050
31.9%
satan 452
 
2.9%
portsweep 286
 
1.8%
smurf 265
 
1.7%
ipsweep 130
 
0.8%
back 130
 
0.8%
warezclient 123
 
0.8%
teardrop 118
 
0.7%
nmap 108
 
0.7%
Other values (8) 58
 
0.4%

Most occurring characters

ValueCountFrequency (%)
n 19898
19.8%
e 11322
11.2%
a 10513
10.4%
r 10036
10.0%
o 9564
9.5%
m 9487
9.4%
l 9250
9.2%
p 6148
 
6.1%
t 6035
 
6.0%
u 5331
 
5.3%
Other values (12) 3096
 
3.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 100680
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 19898
19.8%
e 11322
11.2%
a 10513
10.4%
r 10036
10.0%
o 9564
9.5%
m 9487
9.4%
l 9250
9.2%
p 6148
 
6.1%
t 6035
 
6.0%
u 5331
 
5.3%
Other values (12) 3096
 
3.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 100680
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 19898
19.8%
e 11322
11.2%
a 10513
10.4%
r 10036
10.0%
o 9564
9.5%
m 9487
9.4%
l 9250
9.2%
p 6148
 
6.1%
t 6035
 
6.0%
u 5331
 
5.3%
Other values (12) 3096
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 100680
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 19898
19.8%
e 11322
11.2%
a 10513
10.4%
r 10036
10.0%
o 9564
9.5%
m 9487
9.4%
l 9250
9.2%
p 6148
 
6.1%
t 6035
 
6.0%
u 5331
 
5.3%
Other values (12) 3096
 
3.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
1.0
15697 
0.0
2472 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters54507
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
1.0 15697
86.4%
0.0 2472
 
13.6%

Length

2024-10-01T15:15:48.525407image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-01T15:15:48.608005image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
1.0 15697
86.4%
0.0 2472
 
13.6%

Most occurring characters

ValueCountFrequency (%)
0 20641
37.9%
. 18169
33.3%
1 15697
28.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 20641
37.9%
. 18169
33.3%
1 15697
28.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 20641
37.9%
. 18169
33.3%
1 15697
28.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 20641
37.9%
. 18169
33.3%
1 15697
28.8%

protocol_type_udp
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
16287 
1.0
1882 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters54507
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 16287
89.6%
1.0 1882
 
10.4%

Length

2024-10-01T15:15:48.700652image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-01T15:15:48.787904image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 16287
89.6%
1.0 1882
 
10.4%

Most occurring characters

ValueCountFrequency (%)
0 34456
63.2%
. 18169
33.3%
1 1882
 
3.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 34456
63.2%
. 18169
33.3%
1 1882
 
3.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 34456
63.2%
. 18169
33.3%
1 1882
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 34456
63.2%
. 18169
33.3%
1 1882
 
3.5%

service_X11
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
18165 
1.0
 
4

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters54507
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 18165
> 99.9%
1.0 4
 
< 0.1%

Length

2024-10-01T15:15:48.887072image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-01T15:15:48.974010image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 18165
> 99.9%
1.0 4
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 36334
66.7%
. 18169
33.3%
1 4
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 36334
66.7%
. 18169
33.3%
1 4
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 36334
66.7%
. 18169
33.3%
1 4
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 36334
66.7%
. 18169
33.3%
1 4
 
< 0.1%

service_Z39_50
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
18046 
1.0
 
123

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters54507
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 18046
99.3%
1.0 123
 
0.7%

Length

2024-10-01T15:15:49.066515image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-01T15:15:49.150769image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 18046
99.3%
1.0 123
 
0.7%

Most occurring characters

ValueCountFrequency (%)
0 36215
66.4%
. 18169
33.3%
1 123
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 36215
66.4%
. 18169
33.3%
1 123
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 36215
66.4%
. 18169
33.3%
1 123
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 36215
66.4%
. 18169
33.3%
1 123
 
0.2%

service_auth
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
18029 
1.0
 
140

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters54507
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 18029
99.2%
1.0 140
 
0.8%

Length

2024-10-01T15:15:49.244824image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-01T15:15:49.329861image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 18029
99.2%
1.0 140
 
0.8%

Most occurring characters

ValueCountFrequency (%)
0 36198
66.4%
. 18169
33.3%
1 140
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 36198
66.4%
. 18169
33.3%
1 140
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 36198
66.4%
. 18169
33.3%
1 140
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 36198
66.4%
. 18169
33.3%
1 140
 
0.3%

service_bgp
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
18087 
1.0
 
82

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters54507
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 18087
99.5%
1.0 82
 
0.5%

Length

2024-10-01T15:15:49.419054image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-01T15:15:49.502270image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 18087
99.5%
1.0 82
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 36256
66.5%
. 18169
33.3%
1 82
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 36256
66.5%
. 18169
33.3%
1 82
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 36256
66.5%
. 18169
33.3%
1 82
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 36256
66.5%
. 18169
33.3%
1 82
 
0.2%

service_courier
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
18066 
1.0
 
103

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters54507
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 18066
99.4%
1.0 103
 
0.6%

Length

2024-10-01T15:15:49.592091image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-01T15:15:49.676331image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 18066
99.4%
1.0 103
 
0.6%

Most occurring characters

ValueCountFrequency (%)
0 36235
66.5%
. 18169
33.3%
1 103
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 36235
66.5%
. 18169
33.3%
1 103
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 36235
66.5%
. 18169
33.3%
1 103
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 36235
66.5%
. 18169
33.3%
1 103
 
0.2%

service_csnet_ns
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
18099 
1.0
 
70

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters54507
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 18099
99.6%
1.0 70
 
0.4%

Length

2024-10-01T15:15:49.764006image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-01T15:15:49.849373image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 18099
99.6%
1.0 70
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 36268
66.5%
. 18169
33.3%
1 70
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 36268
66.5%
. 18169
33.3%
1 70
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 36268
66.5%
. 18169
33.3%
1 70
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 36268
66.5%
. 18169
33.3%
1 70
 
0.1%

service_ctf
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
18090 
1.0
 
79

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters54507
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 18090
99.6%
1.0 79
 
0.4%

Length

2024-10-01T15:15:49.937276image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-01T15:15:50.023262image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 18090
99.6%
1.0 79
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 36259
66.5%
. 18169
33.3%
1 79
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 36259
66.5%
. 18169
33.3%
1 79
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 36259
66.5%
. 18169
33.3%
1 79
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 36259
66.5%
. 18169
33.3%
1 79
 
0.1%

service_daytime
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
18104 
1.0
 
65

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters54507
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 18104
99.6%
1.0 65
 
0.4%

Length

2024-10-01T15:15:50.111801image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-01T15:15:50.196023image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 18104
99.6%
1.0 65
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 36273
66.5%
. 18169
33.3%
1 65
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 36273
66.5%
. 18169
33.3%
1 65
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 36273
66.5%
. 18169
33.3%
1 65
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 36273
66.5%
. 18169
33.3%
1 65
 
0.1%

service_discard
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
18100 
1.0
 
69

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters54507
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 18100
99.6%
1.0 69
 
0.4%

Length

2024-10-01T15:15:50.285769image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-01T15:15:50.370916image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 18100
99.6%
1.0 69
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 36269
66.5%
. 18169
33.3%
1 69
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 36269
66.5%
. 18169
33.3%
1 69
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 36269
66.5%
. 18169
33.3%
1 69
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 36269
66.5%
. 18169
33.3%
1 69
 
0.1%

service_domain
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
18109 
1.0
 
60

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters54507
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 18109
99.7%
1.0 60
 
0.3%

Length

2024-10-01T15:15:50.462207image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-01T15:15:50.547773image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 18109
99.7%
1.0 60
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 36278
66.6%
. 18169
33.3%
1 60
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 36278
66.6%
. 18169
33.3%
1 60
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 36278
66.6%
. 18169
33.3%
1 60
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 36278
66.6%
. 18169
33.3%
1 60
 
0.1%

service_domain_u
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
17053 
1.0
 
1116

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters54507
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 17053
93.9%
1.0 1116
 
6.1%

Length

2024-10-01T15:15:50.636588image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-01T15:15:50.721705image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 17053
93.9%
1.0 1116
 
6.1%

Most occurring characters

ValueCountFrequency (%)
0 35222
64.6%
. 18169
33.3%
1 1116
 
2.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 35222
64.6%
. 18169
33.3%
1 1116
 
2.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 35222
64.6%
. 18169
33.3%
1 1116
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 35222
64.6%
. 18169
33.3%
1 1116
 
2.0%

service_echo
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
18132 
1.0
 
37

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters54507
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 18132
99.8%
1.0 37
 
0.2%

Length

2024-10-01T15:15:50.810783image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-01T15:15:50.894286image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 18132
99.8%
1.0 37
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 36301
66.6%
. 18169
33.3%
1 37
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 36301
66.6%
. 18169
33.3%
1 37
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 36301
66.6%
. 18169
33.3%
1 37
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 36301
66.6%
. 18169
33.3%
1 37
 
0.1%

service_eco_i
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
17997 
1.0
 
172

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters54507
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 17997
99.1%
1.0 172
 
0.9%

Length

2024-10-01T15:15:50.983487image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-01T15:15:51.067871image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 17997
99.1%
1.0 172
 
0.9%

Most occurring characters

ValueCountFrequency (%)
0 36166
66.4%
. 18169
33.3%
1 172
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 36166
66.4%
. 18169
33.3%
1 172
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 36166
66.4%
. 18169
33.3%
1 172
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 36166
66.4%
. 18169
33.3%
1 172
 
0.3%

service_ecr_i
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
17838 
1.0
 
331

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters54507
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 17838
98.2%
1.0 331
 
1.8%

Length

2024-10-01T15:15:51.157786image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-01T15:15:51.240911image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 17838
98.2%
1.0 331
 
1.8%

Most occurring characters

ValueCountFrequency (%)
0 36007
66.1%
. 18169
33.3%
1 331
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 36007
66.1%
. 18169
33.3%
1 331
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 36007
66.1%
. 18169
33.3%
1 331
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 36007
66.1%
. 18169
33.3%
1 331
 
0.6%

service_efs
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
18117 
1.0
 
52

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters54507
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 18117
99.7%
1.0 52
 
0.3%

Length

2024-10-01T15:15:51.333966image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-01T15:15:51.415674image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 18117
99.7%
1.0 52
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 36286
66.6%
. 18169
33.3%
1 52
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 36286
66.6%
. 18169
33.3%
1 52
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 36286
66.6%
. 18169
33.3%
1 52
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 36286
66.6%
. 18169
33.3%
1 52
 
0.1%

service_exec
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
18114 
1.0
 
55

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters54507
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 18114
99.7%
1.0 55
 
0.3%

Length

2024-10-01T15:15:51.506184image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-01T15:15:51.595537image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 18114
99.7%
1.0 55
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 36283
66.6%
. 18169
33.3%
1 55
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 36283
66.6%
. 18169
33.3%
1 55
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 36283
66.6%
. 18169
33.3%
1 55
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 36283
66.6%
. 18169
33.3%
1 55
 
0.1%

service_finger
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
17916 
1.0
 
253

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters54507
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 17916
98.6%
1.0 253
 
1.4%

Length

2024-10-01T15:15:51.691024image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-01T15:15:51.778468image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 17916
98.6%
1.0 253
 
1.4%

Most occurring characters

ValueCountFrequency (%)
0 36085
66.2%
. 18169
33.3%
1 253
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 36085
66.2%
. 18169
33.3%
1 253
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 36085
66.2%
. 18169
33.3%
1 253
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 36085
66.2%
. 18169
33.3%
1 253
 
0.5%

service_ftp
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
17928 
1.0
 
241

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters54507
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 17928
98.7%
1.0 241
 
1.3%

Length

2024-10-01T15:15:51.869981image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-01T15:15:51.952561image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 17928
98.7%
1.0 241
 
1.3%

Most occurring characters

ValueCountFrequency (%)
0 36097
66.2%
. 18169
33.3%
1 241
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 36097
66.2%
. 18169
33.3%
1 241
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 36097
66.2%
. 18169
33.3%
1 241
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 36097
66.2%
. 18169
33.3%
1 241
 
0.4%

service_ftp_data
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
17227 
1.0
 
942

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters54507
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 17227
94.8%
1.0 942
 
5.2%

Length

2024-10-01T15:15:52.042992image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-01T15:15:52.129563image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 17227
94.8%
1.0 942
 
5.2%

Most occurring characters

ValueCountFrequency (%)
0 35396
64.9%
. 18169
33.3%
1 942
 
1.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 35396
64.9%
. 18169
33.3%
1 942
 
1.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 35396
64.9%
. 18169
33.3%
1 942
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 35396
64.9%
. 18169
33.3%
1 942
 
1.7%

service_gopher
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
18080 
1.0
 
89

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters54507
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 18080
99.5%
1.0 89
 
0.5%

Length

2024-10-01T15:15:52.218316image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-01T15:15:52.304679image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 18080
99.5%
1.0 89
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 36249
66.5%
. 18169
33.3%
1 89
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 36249
66.5%
. 18169
33.3%
1 89
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 36249
66.5%
. 18169
33.3%
1 89
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 36249
66.5%
. 18169
33.3%
1 89
 
0.2%

service_hostnames
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
18107 
1.0
 
62

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters54507
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 18107
99.7%
1.0 62
 
0.3%

Length

2024-10-01T15:15:52.394518image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-01T15:15:52.479321image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 18107
99.7%
1.0 62
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 36276
66.6%
. 18169
33.3%
1 62
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 36276
66.6%
. 18169
33.3%
1 62
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 36276
66.6%
. 18169
33.3%
1 62
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 36276
66.6%
. 18169
33.3%
1 62
 
0.1%

service_http
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
12651 
1.0
5518 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters54507
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 12651
69.6%
1.0 5518
30.4%

Length

2024-10-01T15:15:52.567794image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-01T15:15:52.654702image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 12651
69.6%
1.0 5518
30.4%

Most occurring characters

ValueCountFrequency (%)
0 30820
56.5%
. 18169
33.3%
1 5518
 
10.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 30820
56.5%
. 18169
33.3%
1 5518
 
10.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 30820
56.5%
. 18169
33.3%
1 5518
 
10.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 30820
56.5%
. 18169
33.3%
1 5518
 
10.1%

service_http_443
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
18099 
1.0
 
70

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters54507
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 18099
99.6%
1.0 70
 
0.4%

Length

2024-10-01T15:15:52.746217image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-01T15:15:52.830301image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 18099
99.6%
1.0 70
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 36268
66.5%
. 18169
33.3%
1 70
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 36268
66.5%
. 18169
33.3%
1 70
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 36268
66.5%
. 18169
33.3%
1 70
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 36268
66.5%
. 18169
33.3%
1 70
 
0.1%

service_http_8001
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
18168 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters54507
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 18168
> 99.9%
1.0 1
 
< 0.1%

Length

2024-10-01T15:15:52.919255image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-01T15:15:53.003690image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 18168
> 99.9%
1.0 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 36337
66.7%
. 18169
33.3%
1 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 36337
66.7%
. 18169
33.3%
1 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 36337
66.7%
. 18169
33.3%
1 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 36337
66.7%
. 18169
33.3%
1 1
 
< 0.1%

service_imap4
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
18087 
1.0
 
82

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters54507
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 18087
99.5%
1.0 82
 
0.5%

Length

2024-10-01T15:15:53.094508image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-01T15:15:53.180635image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 18087
99.5%
1.0 82
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 36256
66.5%
. 18169
33.3%
1 82
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 36256
66.5%
. 18169
33.3%
1 82
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 36256
66.5%
. 18169
33.3%
1 82
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 36256
66.5%
. 18169
33.3%
1 82
 
0.2%

service_iso_tsap
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
18084 
1.0
 
85

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters54507
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 18084
99.5%
1.0 85
 
0.5%

Length

2024-10-01T15:15:53.269637image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-01T15:15:53.355300image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 18084
99.5%
1.0 85
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 36253
66.5%
. 18169
33.3%
1 85
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 36253
66.5%
. 18169
33.3%
1 85
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 36253
66.5%
. 18169
33.3%
1 85
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 36253
66.5%
. 18169
33.3%
1 85
 
0.2%

service_klogin
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
18113 
1.0
 
56

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters54507
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 18113
99.7%
1.0 56
 
0.3%

Length

2024-10-01T15:15:53.444395image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-01T15:15:53.528370image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 18113
99.7%
1.0 56
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 36282
66.6%
. 18169
33.3%
1 56
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 36282
66.6%
. 18169
33.3%
1 56
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 36282
66.6%
. 18169
33.3%
1 56
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 36282
66.6%
. 18169
33.3%
1 56
 
0.1%

service_kshell
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
18132 
1.0
 
37

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters54507
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 18132
99.8%
1.0 37
 
0.2%

Length

2024-10-01T15:15:53.617608image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-01T15:15:53.702933image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 18132
99.8%
1.0 37
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 36301
66.6%
. 18169
33.3%
1 37
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 36301
66.6%
. 18169
33.3%
1 37
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 36301
66.6%
. 18169
33.3%
1 37
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 36301
66.6%
. 18169
33.3%
1 37
 
0.1%

service_ldap
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
18114 
1.0
 
55

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters54507
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 18114
99.7%
1.0 55
 
0.3%

Length

2024-10-01T15:15:53.793975image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-01T15:15:53.876675image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 18114
99.7%
1.0 55
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 36283
66.6%
. 18169
33.3%
1 55
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 36283
66.6%
. 18169
33.3%
1 55
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 36283
66.6%
. 18169
33.3%
1 55
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 36283
66.6%
. 18169
33.3%
1 55
 
0.1%

service_link
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
18105 
1.0
 
64

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters54507
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 18105
99.6%
1.0 64
 
0.4%

Length

2024-10-01T15:15:53.966332image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-01T15:15:54.050736image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 18105
99.6%
1.0 64
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 36274
66.5%
. 18169
33.3%
1 64
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 36274
66.5%
. 18169
33.3%
1 64
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 36274
66.5%
. 18169
33.3%
1 64
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 36274
66.5%
. 18169
33.3%
1 64
 
0.1%

service_login
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
18097 
1.0
 
72

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters54507
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 18097
99.6%
1.0 72
 
0.4%

Length

2024-10-01T15:15:54.145867image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-01T15:15:54.233946image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 18097
99.6%
1.0 72
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 36266
66.5%
. 18169
33.3%
1 72
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 36266
66.5%
. 18169
33.3%
1 72
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 36266
66.5%
. 18169
33.3%
1 72
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 36266
66.5%
. 18169
33.3%
1 72
 
0.1%

service_mtp
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
18110 
1.0
 
59

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters54507
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 18110
99.7%
1.0 59
 
0.3%

Length

2024-10-01T15:15:54.328830image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-01T15:15:54.415432image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 18110
99.7%
1.0 59
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 36279
66.6%
. 18169
33.3%
1 59
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 36279
66.6%
. 18169
33.3%
1 59
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 36279
66.6%
. 18169
33.3%
1 59
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 36279
66.6%
. 18169
33.3%
1 59
 
0.1%

service_name
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
18114 
1.0
 
55

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters54507
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 18114
99.7%
1.0 55
 
0.3%

Length

2024-10-01T15:15:54.511104image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-01T15:15:54.597176image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 18114
99.7%
1.0 55
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 36283
66.6%
. 18169
33.3%
1 55
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 36283
66.6%
. 18169
33.3%
1 55
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 36283
66.6%
. 18169
33.3%
1 55
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 36283
66.6%
. 18169
33.3%
1 55
 
0.1%

service_netbios_dgm
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
18111 
1.0
 
58

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters54507
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 18111
99.7%
1.0 58
 
0.3%

Length

2024-10-01T15:15:54.693603image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-01T15:15:54.779190image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 18111
99.7%
1.0 58
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 36280
66.6%
. 18169
33.3%
1 58
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 36280
66.6%
. 18169
33.3%
1 58
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 36280
66.6%
. 18169
33.3%
1 58
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 36280
66.6%
. 18169
33.3%
1 58
 
0.1%

service_netbios_ns
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
18130 
1.0
 
39

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters54507
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 18130
99.8%
1.0 39
 
0.2%

Length

2024-10-01T15:15:54.866739image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-01T15:15:54.951844image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 18130
99.8%
1.0 39
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 36299
66.6%
. 18169
33.3%
1 39
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 36299
66.6%
. 18169
33.3%
1 39
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 36299
66.6%
. 18169
33.3%
1 39
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 36299
66.6%
. 18169
33.3%
1 39
 
0.1%

service_netbios_ssn
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
18117 
1.0
 
52

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters54507
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 18117
99.7%
1.0 52
 
0.3%

Length

2024-10-01T15:15:55.041082image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-01T15:15:55.126117image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 18117
99.7%
1.0 52
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 36286
66.6%
. 18169
33.3%
1 52
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 36286
66.6%
. 18169
33.3%
1 52
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 36286
66.6%
. 18169
33.3%
1 52
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 36286
66.6%
. 18169
33.3%
1 52
 
0.1%

service_netstat
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
18127 
1.0
 
42

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters54507
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 18127
99.8%
1.0 42
 
0.2%

Length

2024-10-01T15:15:55.215144image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-01T15:15:55.298532image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 18127
99.8%
1.0 42
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 36296
66.6%
. 18169
33.3%
1 42
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 36296
66.6%
. 18169
33.3%
1 42
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 36296
66.6%
. 18169
33.3%
1 42
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 36296
66.6%
. 18169
33.3%
1 42
 
0.1%

service_nnsp
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
18090 
1.0
 
79

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters54507
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 18090
99.6%
1.0 79
 
0.4%

Length

2024-10-01T15:15:55.390176image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-01T15:15:55.474522image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 18090
99.6%
1.0 79
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 36259
66.5%
. 18169
33.3%
1 79
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 36259
66.5%
. 18169
33.3%
1 79
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 36259
66.5%
. 18169
33.3%
1 79
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 36259
66.5%
. 18169
33.3%
1 79
 
0.1%

service_nntp
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
18129 
1.0
 
40

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters54507
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 18129
99.8%
1.0 40
 
0.2%

Length

2024-10-01T15:15:55.562858image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-01T15:15:55.648545image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 18129
99.8%
1.0 40
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 36298
66.6%
. 18169
33.3%
1 40
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 36298
66.6%
. 18169
33.3%
1 40
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 36298
66.6%
. 18169
33.3%
1 40
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 36298
66.6%
. 18169
33.3%
1 40
 
0.1%

service_ntp_u
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
18147 
1.0
 
22

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters54507
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 18147
99.9%
1.0 22
 
0.1%

Length

2024-10-01T15:15:55.738472image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-01T15:15:55.822234image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 18147
99.9%
1.0 22
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 36316
66.6%
. 18169
33.3%
1 22
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 36316
66.6%
. 18169
33.3%
1 22
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 36316
66.6%
. 18169
33.3%
1 22
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 36316
66.6%
. 18169
33.3%
1 22
 
< 0.1%

service_other
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
17696 
1.0
 
473

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters54507
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 17696
97.4%
1.0 473
 
2.6%

Length

2024-10-01T15:15:55.911523image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-01T15:15:55.997239image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 17696
97.4%
1.0 473
 
2.6%

Most occurring characters

ValueCountFrequency (%)
0 35865
65.8%
. 18169
33.3%
1 473
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 35865
65.8%
. 18169
33.3%
1 473
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 35865
65.8%
. 18169
33.3%
1 473
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 35865
65.8%
. 18169
33.3%
1 473
 
0.9%

service_pm_dump
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
18167 
1.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters54507
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 18167
> 99.9%
1.0 2
 
< 0.1%

Length

2024-10-01T15:15:56.085858image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-01T15:15:56.169717image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 18167
> 99.9%
1.0 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 36336
66.7%
. 18169
33.3%
1 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 36336
66.7%
. 18169
33.3%
1 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 36336
66.7%
. 18169
33.3%
1 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 36336
66.7%
. 18169
33.3%
1 2
 
< 0.1%

service_pop_2
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
18161 
1.0
 
8

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters54507
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 18161
> 99.9%
1.0 8
 
< 0.1%

Length

2024-10-01T15:15:56.650455image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-01T15:15:56.721888image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 18161
> 99.9%
1.0 8
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 36330
66.7%
. 18169
33.3%
1 8
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 36330
66.7%
. 18169
33.3%
1 8
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 36330
66.7%
. 18169
33.3%
1 8
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 36330
66.7%
. 18169
33.3%
1 8
 
< 0.1%

service_pop_3
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
18134 
1.0
 
35

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters54507
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 18134
99.8%
1.0 35
 
0.2%

Length

2024-10-01T15:15:56.804056image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-01T15:15:56.879406image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 18134
99.8%
1.0 35
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 36303
66.6%
. 18169
33.3%
1 35
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 36303
66.6%
. 18169
33.3%
1 35
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 36303
66.6%
. 18169
33.3%
1 35
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 36303
66.6%
. 18169
33.3%
1 35
 
0.1%

service_printer
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
18159 
1.0
 
10

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters54507
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 18159
99.9%
1.0 10
 
0.1%

Length

2024-10-01T15:15:56.961315image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-01T15:15:57.036449image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 18159
99.9%
1.0 10
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 36328
66.6%
. 18169
33.3%
1 10
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 36328
66.6%
. 18169
33.3%
1 10
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 36328
66.6%
. 18169
33.3%
1 10
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 36328
66.6%
. 18169
33.3%
1 10
 
< 0.1%

service_private
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
15746 
1.0
2423 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters54507
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 15746
86.7%
1.0 2423
 
13.3%

Length

2024-10-01T15:15:57.164700image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-01T15:15:57.289815image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 15746
86.7%
1.0 2423
 
13.3%

Most occurring characters

ValueCountFrequency (%)
0 33915
62.2%
. 18169
33.3%
1 2423
 
4.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 33915
62.2%
. 18169
33.3%
1 2423
 
4.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 33915
62.2%
. 18169
33.3%
1 2423
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 33915
62.2%
. 18169
33.3%
1 2423
 
4.4%

service_red_i
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
18167 
1.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters54507
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 18167
> 99.9%
1.0 2
 
< 0.1%

Length

2024-10-01T15:15:57.422810image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-01T15:15:57.515404image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 18167
> 99.9%
1.0 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 36336
66.7%
. 18169
33.3%
1 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 36336
66.7%
. 18169
33.3%
1 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 36336
66.7%
. 18169
33.3%
1 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 36336
66.7%
. 18169
33.3%
1 2
 
< 0.1%

service_remote_job
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
18156 
1.0
 
13

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters54507
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 18156
99.9%
1.0 13
 
0.1%

Length

2024-10-01T15:15:57.606706image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-01T15:15:57.683215image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 18156
99.9%
1.0 13
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 36325
66.6%
. 18169
33.3%
1 13
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 36325
66.6%
. 18169
33.3%
1 13
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 36325
66.6%
. 18169
33.3%
1 13
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 36325
66.6%
. 18169
33.3%
1 13
 
< 0.1%

service_rje
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
18163 
1.0
 
6

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters54507
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 18163
> 99.9%
1.0 6
 
< 0.1%

Length

2024-10-01T15:15:57.770352image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-01T15:15:57.853640image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 18163
> 99.9%
1.0 6
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 36332
66.7%
. 18169
33.3%
1 6
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 36332
66.7%
. 18169
33.3%
1 6
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 36332
66.7%
. 18169
33.3%
1 6
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 36332
66.7%
. 18169
33.3%
1 6
 
< 0.1%

service_shell
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
18162 
1.0
 
7

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters54507
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 18162
> 99.9%
1.0 7
 
< 0.1%

Length

2024-10-01T15:15:57.945322image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-01T15:15:58.027405image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 18162
> 99.9%
1.0 7
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 36331
66.7%
. 18169
33.3%
1 7
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 36331
66.7%
. 18169
33.3%
1 7
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 36331
66.7%
. 18169
33.3%
1 7
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 36331
66.7%
. 18169
33.3%
1 7
 
< 0.1%

service_smtp
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
17109 
1.0
 
1060

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters54507
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row1.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 17109
94.2%
1.0 1060
 
5.8%

Length

2024-10-01T15:15:58.120006image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-01T15:15:58.206171image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 17109
94.2%
1.0 1060
 
5.8%

Most occurring characters

ValueCountFrequency (%)
0 35278
64.7%
. 18169
33.3%
1 1060
 
1.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 35278
64.7%
. 18169
33.3%
1 1060
 
1.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 35278
64.7%
. 18169
33.3%
1 1060
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 35278
64.7%
. 18169
33.3%
1 1060
 
1.9%

service_sql_net
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
18142 
1.0
 
27

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters54507
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 18142
99.9%
1.0 27
 
0.1%

Length

2024-10-01T15:15:58.298381image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-01T15:15:58.381807image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 18142
99.9%
1.0 27
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 36311
66.6%
. 18169
33.3%
1 27
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 36311
66.6%
. 18169
33.3%
1 27
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 36311
66.6%
. 18169
33.3%
1 27
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 36311
66.6%
. 18169
33.3%
1 27
 
< 0.1%

service_ssh
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
18130 
1.0
 
39

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters54507
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 18130
99.8%
1.0 39
 
0.2%

Length

2024-10-01T15:15:58.474537image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-01T15:15:58.558879image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 18130
99.8%
1.0 39
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 36299
66.6%
. 18169
33.3%
1 39
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 36299
66.6%
. 18169
33.3%
1 39
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 36299
66.6%
. 18169
33.3%
1 39
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 36299
66.6%
. 18169
33.3%
1 39
 
0.1%

service_sunrpc
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
18109 
1.0
 
60

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters54507
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 18109
99.7%
1.0 60
 
0.3%

Length

2024-10-01T15:15:58.651245image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-01T15:15:58.737917image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 18109
99.7%
1.0 60
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 36278
66.6%
. 18169
33.3%
1 60
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 36278
66.6%
. 18169
33.3%
1 60
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 36278
66.6%
. 18169
33.3%
1 60
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 36278
66.6%
. 18169
33.3%
1 60
 
0.1%

service_supdup
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
18090 
1.0
 
79

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters54507
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 18090
99.6%
1.0 79
 
0.4%

Length

2024-10-01T15:15:58.829983image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-01T15:15:58.914360image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 18090
99.6%
1.0 79
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 36259
66.5%
. 18169
33.3%
1 79
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 36259
66.5%
. 18169
33.3%
1 79
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 36259
66.5%
. 18169
33.3%
1 79
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 36259
66.5%
. 18169
33.3%
1 79
 
0.1%

service_systat
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
18106 
1.0
 
63

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters54507
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 18106
99.7%
1.0 63
 
0.3%

Length

2024-10-01T15:15:59.006810image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-01T15:15:59.097216image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 18106
99.7%
1.0 63
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 36275
66.6%
. 18169
33.3%
1 63
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 36275
66.6%
. 18169
33.3%
1 63
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 36275
66.6%
. 18169
33.3%
1 63
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 36275
66.6%
. 18169
33.3%
1 63
 
0.1%

service_telnet
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
17815 
1.0
 
354

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters54507
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 17815
98.1%
1.0 354
 
1.9%

Length

2024-10-01T15:15:59.189245image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-01T15:15:59.275434image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 17815
98.1%
1.0 354
 
1.9%

Most occurring characters

ValueCountFrequency (%)
0 35984
66.0%
. 18169
33.3%
1 354
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 35984
66.0%
. 18169
33.3%
1 354
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 35984
66.0%
. 18169
33.3%
1 354
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 35984
66.0%
. 18169
33.3%
1 354
 
0.6%

service_tim_i
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
18167 
1.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters54507
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 18167
> 99.9%
1.0 2
 
< 0.1%

Length

2024-10-01T15:15:59.366894image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-01T15:15:59.452965image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 18167
> 99.9%
1.0 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 36336
66.7%
. 18169
33.3%
1 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 36336
66.7%
. 18169
33.3%
1 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 36336
66.7%
. 18169
33.3%
1 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 36336
66.7%
. 18169
33.3%
1 2
 
< 0.1%

service_time
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
18086 
1.0
 
83

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters54507
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 18086
99.5%
1.0 83
 
0.5%

Length

2024-10-01T15:15:59.544121image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-01T15:15:59.629465image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 18086
99.5%
1.0 83
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 36255
66.5%
. 18169
33.3%
1 83
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 36255
66.5%
. 18169
33.3%
1 83
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 36255
66.5%
. 18169
33.3%
1 83
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 36255
66.5%
. 18169
33.3%
1 83
 
0.2%

service_urp_i
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
18086 
1.0
 
83

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters54507
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 18086
99.5%
1.0 83
 
0.5%

Length

2024-10-01T15:15:59.718852image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-01T15:15:59.804668image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 18086
99.5%
1.0 83
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 36255
66.5%
. 18169
33.3%
1 83
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 36255
66.5%
. 18169
33.3%
1 83
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 36255
66.5%
. 18169
33.3%
1 83
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 36255
66.5%
. 18169
33.3%
1 83
 
0.2%

service_uucp
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
18071 
1.0
 
98

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters54507
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 18071
99.5%
1.0 98
 
0.5%

Length

2024-10-01T15:15:59.897482image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-01T15:15:59.987435image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 18071
99.5%
1.0 98
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 36240
66.5%
. 18169
33.3%
1 98
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 36240
66.5%
. 18169
33.3%
1 98
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 36240
66.5%
. 18169
33.3%
1 98
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 36240
66.5%
. 18169
33.3%
1 98
 
0.2%

service_uucp_path
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
18085 
1.0
 
84

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters54507
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 18085
99.5%
1.0 84
 
0.5%

Length

2024-10-01T15:16:00.081757image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-01T15:16:00.167822image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 18085
99.5%
1.0 84
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 36254
66.5%
. 18169
33.3%
1 84
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 36254
66.5%
. 18169
33.3%
1 84
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 36254
66.5%
. 18169
33.3%
1 84
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 36254
66.5%
. 18169
33.3%
1 84
 
0.2%

service_vmnet
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
18083 
1.0
 
86

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters54507
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 18083
99.5%
1.0 86
 
0.5%

Length

2024-10-01T15:16:00.261906image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-01T15:16:00.350569image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 18083
99.5%
1.0 86
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 36252
66.5%
. 18169
33.3%
1 86
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 36252
66.5%
. 18169
33.3%
1 86
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 36252
66.5%
. 18169
33.3%
1 86
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 36252
66.5%
. 18169
33.3%
1 86
 
0.2%

service_whois
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
18097 
1.0
 
72

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters54507
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 18097
99.6%
1.0 72
 
0.4%

Length

2024-10-01T15:16:00.450139image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-01T15:16:00.542876image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 18097
99.6%
1.0 72
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 36266
66.5%
. 18169
33.3%
1 72
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 36266
66.5%
. 18169
33.3%
1 72
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 36266
66.5%
. 18169
33.3%
1 72
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 54507
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 36266
66.5%
. 18169
33.3%
1 72
 
0.1%

Interactions

2024-10-01T15:15:42.145575image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:28.791443image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:29.883771image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:30.987327image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:32.056176image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:33.120416image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:34.782757image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:35.755666image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:36.809038image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:37.867684image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:38.961607image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:40.026955image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:41.080982image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:42.221552image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:28.892900image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:29.966528image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:31.070802image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:32.138993image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:33.202385image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:34.855154image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:35.834926image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:36.889406image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:37.948642image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:39.043637image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:40.111656image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:41.164065image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:42.303017image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:28.980127image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:30.051500image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:31.152147image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:32.222530image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:34.022220image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:34.928448image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:35.916407image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:36.977171image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:38.039015image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:39.126211image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:40.195999image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:41.247595image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:42.383132image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:29.059509image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:30.136456image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:31.231532image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:32.303075image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:34.089560image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:35.000101image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:35.995430image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:37.059191image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:38.118153image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:39.206397image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:40.272695image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:41.329603image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:42.459307image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:29.142590image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:30.219661image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:31.312564image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:32.386396image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:34.159554image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:35.068465image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:36.073412image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:37.141175image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:38.200951image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:39.290972image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:40.354354image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:41.411581image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:42.537860image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:29.222042image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:30.303047image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:31.392021image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:32.470372image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:34.228039image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:35.139125image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:36.151249image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:37.224261image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:38.281645image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:39.374886image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:40.434501image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:41.491159image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:42.615636image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:29.307012image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:30.389284image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:31.475164image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:32.549571image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:34.298283image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:35.215062image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:36.235162image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:37.304974image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:38.366927image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:39.458063image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:40.514430image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:41.575810image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:42.694252image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:29.385907image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:30.474897image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:31.561997image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:32.634390image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:34.367424image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:35.293624image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:36.318017image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:37.386646image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:38.455125image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:39.540406image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:40.596668image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:41.656420image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:42.772754image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:29.469491image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:30.561291image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:31.648446image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:32.714370image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:34.438023image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:35.370508image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:36.400259image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:37.468602image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:38.548132image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:39.622119image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:40.677677image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:41.740327image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:42.848252image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:29.549464image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:30.644519image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:31.733215image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:32.795754image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:34.506349image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:35.449177image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:36.485275image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:37.550892image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:38.634746image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:39.702820image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:40.755091image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:41.820922image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:42.928296image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:29.632859image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:30.733390image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:31.812991image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:32.878555image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:34.578751image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:35.525354image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:36.566660image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:37.629661image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:38.720603image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:39.786945image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:40.835787image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:41.904815image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:43.009081image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:29.712045image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:30.819211image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:31.893605image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:32.959854image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:34.647603image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:35.601428image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:36.649153image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:37.709221image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:38.802480image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:39.865354image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:40.916070image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:41.984238image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:43.089696image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:29.800276image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:30.907876image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:31.977754image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:33.040501image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:34.716787image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:35.681167image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:36.730516image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:37.789437image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:38.882484image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:39.947997image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:41.000852image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-01T15:15:42.070396image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Missing values

2024-10-01T15:15:43.331843image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-10-01T15:15:43.764651image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

src_bytesdst_byteslogged_inserror_ratesame_srv_ratediff_srv_ratesrv_diff_host_ratedst_host_countdst_host_same_srv_ratedst_host_diff_srv_ratedst_host_srv_diff_host_ratedst_host_serror_ratedst_host_srv_serror_rateleveloutcomeprotocol_type_tcpprotocol_type_udpservice_X11service_Z39_50service_authservice_bgpservice_courierservice_csnet_nsservice_ctfservice_daytimeservice_discardservice_domainservice_domain_uservice_echoservice_eco_iservice_ecr_iservice_efsservice_execservice_fingerservice_ftpservice_ftp_dataservice_gopherservice_hostnamesservice_httpservice_http_443service_http_8001service_imap4service_iso_tsapservice_kloginservice_kshellservice_ldapservice_linkservice_loginservice_mtpservice_nameservice_netbios_dgmservice_netbios_nsservice_netbios_ssnservice_netstatservice_nnspservice_nntpservice_ntp_uservice_otherservice_pm_dumpservice_pop_2service_pop_3service_printerservice_privateservice_red_iservice_remote_jobservice_rjeservice_shellservice_smtpservice_sql_netservice_sshservice_sunrpcservice_supdupservice_systatservice_telnetservice_tim_iservice_timeservice_urp_iservice_uucpservice_uucp_pathservice_vmnetservice_whois
00.00.00.01.000.110.070.0255.00.020.070.001.001.021.0neptune1.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0
10.00.00.00.000.140.070.0255.00.070.070.000.000.018.0neptune1.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0
2977.0335.01.00.001.000.000.0209.00.570.020.020.000.021.0normal1.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.0
3230.010597.01.00.001.000.000.05.01.000.000.040.000.021.0normal1.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0
446.086.00.00.001.000.010.0255.01.000.010.000.000.018.0normal0.01.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0
5195.02790.01.00.001.000.000.06.01.000.000.050.000.021.0normal1.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0
6857.0338.01.00.001.000.000.086.00.360.060.020.000.021.0normal1.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.0
71032.00.00.00.001.000.000.0255.01.000.000.000.000.018.0smurf0.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0
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src_bytesdst_byteslogged_inserror_ratesame_srv_ratediff_srv_ratesrv_diff_host_ratedst_host_countdst_host_same_srv_ratedst_host_diff_srv_ratedst_host_srv_diff_host_ratedst_host_serror_ratedst_host_srv_serror_rateleveloutcomeprotocol_type_tcpprotocol_type_udpservice_X11service_Z39_50service_authservice_bgpservice_courierservice_csnet_nsservice_ctfservice_daytimeservice_discardservice_domainservice_domain_uservice_echoservice_eco_iservice_ecr_iservice_efsservice_execservice_fingerservice_ftpservice_ftp_dataservice_gopherservice_hostnamesservice_httpservice_http_443service_http_8001service_imap4service_iso_tsapservice_kloginservice_kshellservice_ldapservice_linkservice_loginservice_mtpservice_nameservice_netbios_dgmservice_netbios_nsservice_netbios_ssnservice_netstatservice_nnspservice_nntpservice_ntp_uservice_otherservice_pm_dumpservice_pop_2service_pop_3service_printerservice_privateservice_red_iservice_remote_jobservice_rjeservice_shellservice_smtpservice_sql_netservice_sshservice_sunrpcservice_supdupservice_systatservice_telnetservice_tim_iservice_timeservice_urp_iservice_uucpservice_uucp_pathservice_vmnetservice_whois
1578548.00.00.00.01.00.00.0255.00.540.020.00.00.021.0NaN1.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0
1579148.00.00.00.01.00.00.0255.00.540.020.00.00.021.0NaN1.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0
1579648.00.00.00.01.00.00.0255.00.540.020.00.00.021.0NaN1.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0
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1580948.00.00.00.01.00.00.0255.00.540.020.00.00.021.0NaN1.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0
1581348.00.00.00.01.00.00.0255.00.540.020.00.00.021.0NaN1.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0
1581648.00.00.00.01.00.00.0255.00.540.020.00.00.021.0NaN1.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0
1581748.00.00.00.01.00.00.0255.00.540.020.00.00.021.0NaN1.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.0
1581848.00.00.00.01.00.00.0255.00.540.020.00.00.021.0NaN1.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0
1582648.00.00.00.01.00.00.0255.00.540.020.00.00.021.0NaN1.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0

Duplicate rows

Most frequently occurring

src_bytesdst_byteslogged_inserror_ratesame_srv_ratediff_srv_ratesrv_diff_host_ratedst_host_countdst_host_same_srv_ratedst_host_diff_srv_ratedst_host_srv_diff_host_ratedst_host_serror_ratedst_host_srv_serror_rateleveloutcomeprotocol_type_tcpprotocol_type_udpservice_X11service_Z39_50service_authservice_bgpservice_courierservice_csnet_nsservice_ctfservice_daytimeservice_discardservice_domainservice_domain_uservice_echoservice_eco_iservice_ecr_iservice_efsservice_execservice_fingerservice_ftpservice_ftp_dataservice_gopherservice_hostnamesservice_httpservice_http_443service_http_8001service_imap4service_iso_tsapservice_kloginservice_kshellservice_ldapservice_linkservice_loginservice_mtpservice_nameservice_netbios_dgmservice_netbios_nsservice_netbios_ssnservice_netstatservice_nnspservice_nntpservice_ntp_uservice_otherservice_pm_dumpservice_pop_2service_pop_3service_printerservice_privateservice_red_iservice_remote_jobservice_rjeservice_shellservice_smtpservice_sql_netservice_sshservice_sunrpcservice_supdupservice_systatservice_telnetservice_tim_iservice_timeservice_urp_iservice_uucpservice_uucp_pathservice_vmnetservice_whois# duplicates
27248.00.00.00.01.00.00.0255.00.540.020.00.00.021.0NaN1.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0840
25248.00.00.00.01.00.00.0255.00.540.020.00.00.021.0NaN1.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0285
25048.00.00.00.01.00.00.0255.00.540.020.00.00.021.0NaN1.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.0153
23748.00.00.00.01.00.00.0255.00.540.020.00.00.021.0NaN0.01.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0147
27548.00.00.00.01.00.00.0255.00.540.020.00.00.021.0NaN1.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0135
23448.00.00.00.01.00.00.0255.00.540.020.00.00.021.0NaN0.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.079
23548.00.00.00.01.00.00.0255.00.540.020.00.00.021.0NaN0.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.048
23248.00.00.00.01.00.00.0255.00.540.020.00.00.021.0NaN0.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.045
24448.00.00.00.01.00.00.0255.00.540.020.00.00.021.0NaN1.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.041
27748.00.00.00.01.00.00.0255.00.540.020.00.00.021.0NaN1.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.040